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Publication-only abstracts (abstract number preceded by an "e"), published in conjunction with the 2019 ASCO Annual Meeting but not presented at the Meeting, can be found online only.

Improvement of lung cancer risk prediction adding SNPs to the HUNT Lung Cancer Model: A HUNT Study.

Sub-category:
Metastatic Non-Small Cell Lung Cancer

Category:
Lung Cancer—Non-Small Cell Metastatic

Meeting:
2019 ASCO Annual Meeting

Abstract No:
e20696

Citation:
J Clin Oncol 37, 2019 (suppl; abstr e20696)

Author(s): Oluf D. Røe, Olav Toai Duc Nguyen, Klio Lakiotaki, Ioannis Tsamardinos, Vincenzo Lagani, Maria Markaki; Department of Clinical Research and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway; Cancer Clinic, Levanger Hospital, Nord-Trøndelag Health Trust, Levanger, Norway; University of Crete, Heraklion, Greece; Department of Computer Science, University of Crete, Heraklion, Greece; Institute of Chemical Biology, Ilia State University, Tibilisi, Georgia; University of Thrace, Department of Molecular Biology and Genetics, Alexandroupolis, Greece

Abstract Disclosures

Abstract:

Background: A novel validated model for risk prediction of lung cancer, the HUNT Lung Cancer Model predicts 6- and 16-year risk of lung cancer with a C-index = 0.879 and 6-year AUC = 0.87. The model is valid for smokers and ex-smokers of any intensity and quit time and includes seven variables; age, BMI, pack-years, smoking intensity (cigarettes per day), quit time, daily cough in periods of the year and hours of daily indoors smoke exposure. Genome-wide association studies (GWAS) have consistently identified specific lung cancer susceptibility regions. We aimed to improve performance of the HUNT model by integrating the most significant Single Nucleotide Polymorphisms (SNPs). Methods: Lung cancer cases (n = 484) and controls without other cancer (n = 50337) were genotyped for 22 SNPs located in GWAS-identified lung cancer susceptibility regions. Variable selection and model development used backwards feature selection with Akaike Information Criterion in multivariable Cox regression models. Internal validation used bootstrap to assess the change in area under the receiver operator characteristic curve (AUC) in order to compare nested models with and without genetic variables in the ever-smokers´ population (n = 456 cases, n = 28633 controls). We also used likelihood based methods for significance testing. Results: Variable selection and model development in the general population yielded six SNPs, rs1051730, rs11571833, rs13314271, rs2131877, rs2736100 and rs4488809. The added genetic information from these SNPs to the HUNT model, resulted in an improvement according to F test of the nested models (ANOVA p-value 0.000002425). The AUC of the augmented model was 0.881 (95% CI [0.869 0.892]) vs 0.869 without SNPs. Conclusions: In a highly predictive clinical risk prediction model, the integration of SNPs could further improve model performance according to likelihood based methods. Further refinement and validation of this integrated model is needed for clinical use.

 
Other Abstracts in this Sub-Category:

 

1. Association of STK11/LKB1 genomic alterations with lack of benefit from the addition of pembrolizumab to platinum doublet chemotherapy in non-squamous non-small cell lung cancer.

Meeting: 2019 ASCO Annual Meeting Abstract No: 102 First Author: Ferdinandos Skoulidis
Category: Lung Cancer—Non-Small Cell Metastatic - Metastatic Non-Small Cell Lung Cancer

 

2. Real-world outcomes of patients with advanced non-small cell lung cancer (aNSCLC) and autoimmune disease (AD) receiving immune checkpoint inhibitors (ICIs).

Meeting: 2019 ASCO Annual Meeting Abstract No: 110 First Author: Sean Khozin
Category: Lung Cancer—Non-Small Cell Metastatic - Metastatic Non-Small Cell Lung Cancer

 

3. RELAY: A multinational, double-blind, randomized Phase 3 study of erlotinib (ERL) in combination with ramucirumab (RAM) or placebo (PL) in previously untreated patients with epidermal growth factor receptor mutation-positive (EGFRm) metastatic non-small cell lung cancer (NSCLC).

Meeting: 2019 ASCO Annual Meeting Abstract No: 9000 First Author: Kazuhiko Nakagawa
Category: Lung Cancer—Non-Small Cell Metastatic - Metastatic Non-Small Cell Lung Cancer

 

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